Medical Error and Under-Reporting Causes from the Viewpoints of Nursing Managers: A Qualitative Study
Bibliographic record
Abstract
Background: Patient safety as a goal can be achieved by reporting medical errors (ME); however, most errors are never reported. The aim of this study is to explore the causes of ME, and the obstacles in reporting them amongst nurses. Methods: We conducted semi-structural interviews, with 12 nursing managers in the biggest teaching hospital in southern Iran (2015-2016). The interview guide concentrated on the causes of ME and barriers in reporting them. All face-to-face interviews were recorded and transcribed verbatim and analysed using thematic analysis. Results: In this study 4 main themes were extracted for the causes of ME: personal/social characteristics, nonprofessional practice, hospital related factors/organization contextual factors, and poor management. Also, 5 main themes (such as; personal characteristics, fear from reporting, nonprofessional practices, cultural and social factors, and error surveillance system features) were obtained with regards to barriers in reporting. Conclusions: ME can be reduced by improving professional practice and better human resource management. Also, reporting errors can be increased by focusing on cultural and social factors.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".